An expected value of a measurement in a first context may be inferred based at least partly on a contextual signal. The contextual signal may comprise an actual value that is: (i) of a same type as the expected value, and (ii) associated with a second context that is different from the first context (e.g., the contexts can comprise geographical areas), or the contextual signal may comprise an actual value that is: (i) of a different type than a type of the expected value, and (ii) associated with the first context, or a second context that is different from the first context. If a difference between the expected value and an actual value of the first context is greater than a threshold difference, this condition is considered an anomaly. A detected anomaly may be used to determine an event that may be significant or otherwise of interest to a user community.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A computer-implemented method comprising: collecting a first time series of data points representing actual values for a first geographical area, and a second time series of data points representing actual values for a second geographical area that is different from the first geographical area, the actual values of the first time series and the actual values of the second time series being of a same type; utilizing a machine-learned inference function to infer a third time series of data points representing expected values of the same type as the actual values of the first and second time series, the expected values being for the first geographical area; determining a fourth time series of data points representing prediction errors for the first geographical area; and determining that an anomaly has occurred in the first geographical area based at least in part on the prediction errors, wherein the data points of the first, second, third, and fourth time series correspond to a same plurality of points in time; and wherein, for each of the plurality of points in time, a respective one of the expected values of the third time series at that point in time is inferred based at least in part on a respective one of the actual values of the second time series at that point in time, and a respective one of the prediction errors of the fourth time series at that point in time is determined by comparing the respective one of the expected values of the third time series at that point in time to a respective one of the actual values of the first time series at that point in time.
2. The computer-implemented method of claim 1 , further comprising training the machine-learned inference function to infer the expected values.
3. The computer-implemented method of claim 1 , further comprising: determining a standard deviation of the prediction errors; normalizing the prediction errors by dividing the prediction errors by the standard deviation to obtain normalized prediction errors; and comparing the normalized prediction errors to a predetermined threshold, wherein determining that the anomaly has occurred comprises determining that at least one of the normalized prediction errors exceeds the predetermined threshold.
4. The computer-implemented method of claim 1 , wherein the second geographical area is within a predetermined distance from the first geographical area, the method further comprising determining an event in the first geographical area based at least in part on the anomaly.
5. The computer-implemented method of claim 4 , further comprising: designating the event as a candidate event; and determining that the candidate event is a real event using a machine-learned binary classifier.
6. The computer-implemented method of claim 5 , further comprising: accessing a standing query associated with a user; determining that attributes of the real event satisfy the standing query; and transmitting a notification of the real event to a client device associated with the user.
7. The computer-implemented method of claim 5 , further comprising: creating a text summary of the real event; and causing presentation of the text summary on a public display situated in a public space within the first geographical area.
8. The computer-implemented method of claim 1 , further comprising: discretizing an initial geographical area into a plurality of geographical areas, the plurality of geographical areas including the first geographical area and the second geographical area.
9. The computer-implemented method of claim 8 , wherein the initial geographical area is discretized into a triangular tessellation such that each geographical area of the plurality of geographical areas is triangular, and the plurality of geographical areas are uniform in size.
10. The computer-implemented method of claim 9 , wherein discretizing the initial geographical area comprises creating a hierarchical triangular mesh (HTM) comprising multiple triangular tessellations at different levels of resolution, the method further comprising: discretizing time by creating a hierarchy of time intervals at different durations; combining each time interval of the hierarchy of time intervals with each level of the different levels of resolution to create a plurality of spatio-temporal resolutions; selecting the first geographical area at a spatio-temporal resolution of the plurality of spatio-temporal resolutions; and for the first geographical area: constructing a time series; training the machine-learned inference function; and determining that a qualifying condition is met by analyzing the time series to determine that actual values of the time series meet or exceed a threshold.
11. A computer-implemented method comprising: collecting a first time series of data points representing actual values of a first type for a first context, and a second time series of data points representing actual values of a second type for the first context or for a second context that is different from the first context, the first type being different than the second type; utilizing a machine-learned inference function to infer a third time series of data points representing expected values of the first type for the first context; and determining a fourth time series of data points representing prediction errors; and determining that an anomaly has occurred in the first context based at least in part on the prediction errors, wherein the data points of the first, second, third, and fourth time series correspond to a same plurality of points in time; and wherein, for each of the plurality of points in time, a respective one of the expected values of the third time series at that point in time is inferred based at least in part on a respective one of the actual values of the second time series at that point in time, and a respective one of the prediction errors of the fourth time series at that point in time is determined by comparing the respective one of the expected values of the third time series at that point in time to a respective one of the actual values of the first time series at that point in time.
12. The computer-implemented method of claim 11 , further comprising training the machine-learned inference function to infer the expected values for the first context.
13. The computer-implemented method of claim 11 , further comprising comparing the prediction errors to a predetermined threshold, wherein determining that the anomaly has occurred in the first context comprises determining that at least one of the prediction errors exceeds the predetermined threshold.
14. The computer-implemented method of claim 11 , wherein the first context comprises a first geographical area and the second context comprises a second geographical area that is adjacent to the first geographical area, the method further comprising determining an event in the first geographical area based at least in part on the anomaly.
15. The computer-implemented method of claim 14 , further comprising: designating the event as a candidate event; and determining that the candidate event is a real event using a machine-learned binary classifier.
16. A system comprising: memory; one or more processors; and one or more modules stored in the memory and executable by the one or more processors to perform operations comprising: collecting a first time series of data points representing actual values for a first geographical area and a second time series of data points representing actual values for a second geographical area that is different from the first geographical area, the actual values of the first time series and the actual values of the second time series being of a same type; utilizing a machine-learned inference function, inferring a third time series of data points representing expected values of the same type as the actual values of the first and second time series, the expected values being for the first geographical area; determining a fourth time series of data points representing prediction errors for the first geographical area; and determining that an anomaly has occurred in the first geographical area based at least in part on the time series of prediction errors, wherein the data points of the first, second, third, and fourth time series correspond to a same plurality of points in time; and wherein, for each of the plurality of points in time, a respective one of the expected values of the third time series at that point in time is inferred based at least in part on a respective one of the actual values of the second time series at that point in time, and a respective one of the prediction errors of the fourth time series at that point in time is determined by comparing the respective one of the expected values of the third time series at that point in time to a respective one of the actual values of the first time series at that point in time.
17. The system of claim 16 , the operations further comprising: segmenting an initial geographical area into a plurality of geographical areas, wherein the plurality of geographical areas includes the first geographical area and the second geographical area; constructing the second time series of data points representing actual values for the second geographical area; constructing the first time series of data points representing actual values for the first geographical area; and selecting the first geographical area to analyze for the anomaly.
18. The system of claim 16 , the operations further comprising training the machine-learned inference function to infer the third time series of data points representing expected values for the first geographical area as a function of: a time of day, a day of week, and at least the second time series of data points representing actual values.
19. The system of claim 16 , the operations further comprising determining an event in the first geographical area based at least in part on the anomaly.
20. The system of claim 19 , the operations further comprising: designating the event as a candidate event; and determining that the candidate event is a real event using a machine-learned binary classifier.
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September 16, 2015
April 7, 2020
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